KM-201c

AI Retrieval & Integration

The practitioner course for AI-powered knowledge retrieval. Covers retrieval architecture (why keyword search fails, RAG fundamentals, chunking and embedding strategy), workflow integration (knowledge-connected agents, proactive vs. on-demand surfacing, tool integrations), and retrieval quality measurement (precision and recall, hallucination prevention, feedback loops and continuous improvement).

9 Lessons · ~0.5 Hours · 3 Modules

Instructor: ATLAS — Lead Instructor — Knowledge Management

Module 1: Retrieval Architecture

Why keyword search fails organizational knowledge, how RAG works, and how chunking and embedding strategy determines retrieval quality.

Why Keyword Search Fails Organizational Knowledge (4 min read)
RAG Fundamentals: Retrieval-Augmented Generation for Enterprise Knowledge (5 min read)
Chunking and Embedding Strategy: How Structure Determines Retrieval Quality (5 min read)

Module 2: Workflow Integration

Connecting the knowledge retrieval system to agents, determining when to surface knowledge proactively versus on-demand, and integrating with the tools people already use.

Knowledge-Connected Agents: Giving AI Agents Access to Organizational Knowledge (4 min read)
The Retrieval Interface: Proactive vs. On-Demand Knowledge Surfacing (4 min read)
Integration Patterns: Connecting Knowledge to Slack, CRM, and Docs (4 min read)

Module 3: Measuring Retrieval Quality

The metrics that determine whether retrieval is working, how to detect and prevent AI hallucination from knowledge gaps, and the feedback loops that continuously improve the system.

Retrieval Quality Metrics: Precision, Recall, and Relevance (4 min read)
The Hallucination Problem: Detecting and Preventing AI Fabrication from Knowledge Gaps (4 min read)
Continuous Improvement: Feedback Loops, Knowledge Gap Detection, and Retrieval Monitoring (4 min read)